Reduce the spread risk of covid-19 based on clinical fusion data and monitoring system in wireless sensor network

The expression "COVID-19” has been the fiercest but most trending Google search since it first appeared in November 2019. Due to advances in mobile technology and sensors, Healthcare systems based on the Internet of Things are conceivable. Instead of the traditional reactive healthcare systems, these new healthcare systems can be proactive and preventive. This paper suggested a framework for real-time suspect detection based on the Internet of Things. In the early phases of predicting COVID-19, the framework evaluates the existence of the virus by extracting health variables obtained in real-time from sensors and other IoT devices, in order to better understand the behavior of the virus by collecting symptom data of COVID-19, In this paper, four machine learning models (Random Forest, Decision Tree, K-Nearest Neural Network, and Artificial Neural Network) are proposed, these data and applied as a machine learning model to obtain high diagnostic accuracy, however, it is noted that there is a problem when collecting clinical fusion data that is scarce and unbalanced, so a dataset augmented by Generative Adversarial Network (GAN) was used. Several algorithms achieved high levels of accuracy (ACC), including Random Forest (99%), and Decision Tree (99%), K-Nearest Neighbour (98%), and Artificial Neural Network (99%). These results show the ability of GANs to generate data and their ability to provide relevant data to efficiently manage Covid-19 and reduce the risk of its spread through accurate diagnosis of patients and informing health authorities of suspected cases. © 2023, American Scientific Publishing Group (ASPG). All rights reserved.

Mobile-based medical alert system for COVID-19 based on ZigBee and WiFi

ZigBee technology is preferably been used for health monitoring as it consumes very less power, high reliability, and low expenses. In this paper, mobile-based medical alert system for COVID-19 detection system using ZigBee technology is proposed. The health report of the user will be sent to the caretaker or doctor via cloud computing network so that they can analyze the problem. The real-time monitoring of health temperature and symptoms of COVID-19 and data transmission via remote sensing is also realized. © 2022 Scrivener Publishing LLC.

Multihop Clustering Routing Protocol Based on Improved Coronavirus Herd Immunity Optimizer and Q-Learning in WSNs

Wireless sensor networks (WSNs) are composed of a large number of spatially distributed sensor nodes to monitor and transmit information from the environment. However, the batteries used by these sensor nodes have limited energy and cannot be charged or replaced due to the harsh deployment environment. This energy limitation will seriously affect the lifetime of the network. Therefore, the purpose of this research is to reduce energy consumption and balance the load of sensor nodes by clustering routing protocols, so as to prolong the lifetime of the network. First, the coronavirus herd immune optimizer is improved and used to optimize the network clustering. Second, the cluster heads (CHs) are selected according to the energy and location factors in the clusters, and a reasonable CH replacement mechanism is designed to avoid the extra communication energy consumption caused by the frequent replacement of CHs. Finally, a multihop routing mechanism between the CHs and the base station is constructed by Q-learning. Simulation results show that the proposed work can improve the structure of clusters, enhance the load balance of nodes, reduce network energy consumption, and prolong the network lifetime. The appearance time of the first energy-depleted node is delayed by 25.8%, 85.9%, and 162.2% compared with IGWO, ACA-LEACH, and DEAL in the monitoring area of $300×300 m, respectively. In addition, the proposed protocol shows better adaptability in varying dynamic conditions. © 2001-2012 IEEE.

Detection of COVID-19 Using Screen Printed Electrode based Biosensor

Corona virus (COVID-19) is an infectious disease, now this COVID-19 pandemic got spread all over the world which causes illness in the respiratory system in humans, it can spread widely in a short time. In this paper the concept of wireless sensor network (WSN) for Internet of things (IoT) is allocated to the healthcare and detection system for COVID-19 is used to design the biomedical sensors with microcontrollers which are used to collect the data, biosensor based low-cost sensitive portable devices for COVID-19 testing kit which is based on Screen printed electrode sensor (SPEs), this is the complete model of health professionals are observe patients information at the ThingSpeak with help of Wi-Fi, Bluetooth module, professionals workload is minimizing to reducing the possibility of the infected COVID-19 condition. the performance of this work is the data is monitored by the patient's status, the output of these sensors is communicated via wireless sensing node and acquiring for same data has to be send to the remote wireless monitor for the observed patients status via IoT, If in case of any emergency patients can also control the conditions. The stage of infection disease patients can also monitor system data is to inform the medical professionals at the time being finished. Hence the optimistic results show that the biomedical sensors and SPEs are in beneficial process for identification of COVID-19 so it can be situating the results on ThingSpeak and Bluetooth module, The clinical centers to help conditions behind its conformation with additional biomedical sensors. © 2023 The Authors. Published by AnaPub Publications.

Real-Time Health Monitoring System Using Predictive Analytics

Medical specialists are primarily interested in researching health care as a potential replacement for conventional healthcare methods nowadays. COVID-19 creates chaos in society regardless of the modern technological evaluation involved in this sector. Due to inadequate medical care and timely, accurate prognoses, many unexpected fatalities occur. As medical applications have expanded in their reaches along with their technical revolution, therefore patient monitoring systems are getting more popular among the medical actors. The Internet of Things (IoT) has met the requirements for the solution to deliver such a vast service globally at any time and in any location. The suggested model shows a wearable sensor node that the patients will wear. Monitoring client metrics like blood pressure, heart rate, temperature, etc., is the responsibility of the sensor nodes, which send the data to the cloud via an intermediary node. The sensor-acquired data are stored in the cloud storage for detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Among the different cloud-based predictive models now being used, the model having the highest accuracy will be treated as the resultant model. This resultant model will be further used for the data dissemination mechanism by which the concerned medical actors will be provided an alert message for a proper medication in a desirable manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Networking Data and Intelligent Management in the Post-COVID19 Era: A Report on APNOMS 2021

This article presents a report on APNOMS 2021, which was held on September 8–10, 2021 in Tainan, Taiwan. The theme of APNOMS 2021 was "Networking Data and Intelligent Management in the Post-COVID19 Era.”. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Online physics practicum supported by wireless sensor network

This study proposes an online practicum model supported by Wireless Sensor Network (WSN) to implement a physics practicum after the Covid 19 Pandemic. This system is guided by exploratory inquiry questions to help structure students' mindsets in answering investigative questions. Online practicum is also integrated with video conferencing, chat, evaluation system, and lab inquiry stages. The sensor measurement process is carried out directly via live streaming video, where the sensor measurement results are sent in real-time to the website via an internet connection. This study was conducted on 25 students (10 male and 15 female) who were prospective physics teachers. This study used a pre-experimental method with a one-group pretest and post-test design. The study results show that the online practicum model supported by WSN can effectively increase the inquiry skill of prospective physics teacher students. Usability test results obtained an average score of SUS 91.63, which means the practicum system can be categorized as having a user acceptance level of Excellent.

Distributed localization using normalized quadlateration in LoRa networks: application for tourist position estimation

The tourism sector has been growing rapidly along with the decline of COVID-19. This sector should pay attention to safety and security, which is by tracking the position of tourists. In this work, we propose a tourist positioning tracking system. A node containing a microcontroller with LoRa wireless communication called unknown node (UN) is assembled as a wearable device. We use the received signal strength indicator (RSSI)-based localization technique with an additional normalization method to correct the error rate in position estimation. This method estimates the distance between the anchor node (AN) and UN to approximate the actual distance value. Three-dimensional position tracking (longitude, latitude, altitude) of five UN surrounded by four AN was performed using the quadlateration method. The estimation process was carried out by the UN whereas receiving information is transmitted from the four AN. The communication between AN and UN used a scheduling algorithm on all AN. The position estimation error of the five UNs was measured using mean square error (MSE) yields 20.73 m, 50.32 m, 32.92 m, 21.40 m, and 16.89 m respectively. The accuracy of the estimated distance obtained by the proposed normalization quadlateration method is increased up to 15.22%. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Real-Time Health Monitoring System Using Predictive Analytics

Medical specialists are primarily interested in researching health care as a potential replacement for conventional healthcare methods nowadays. COVID-19 creates chaos in society regardless of the modern technological evaluation involved in this sector. Due to inadequate medical care and timely, accurate prognoses, many unexpected fatalities occur. As medical applications have expanded in their reaches along with their technical revolution, therefore patient monitoring systems are getting more popular among the medical actors. The Internet of Things (IoT) has met the requirements for the solution to deliver such a vast service globally at any time and in any location. The suggested model shows a wearable sensor node that the patients will wear. Monitoring client metrics like blood pressure, heart rate, temperature, etc., is the responsibility of the sensor nodes, which send the data to the cloud via an intermediary node. The sensor-acquired data are stored in the cloud storage for detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Among the different cloud-based predictive models now being used, the model having the highest accuracy will be treated as the resultant model. This resultant model will be further used for the data dissemination mechanism by which the concerned medical actors will be provided an alert message for a proper medication in a desirable manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Dual-Hop Direct Diffusion Routing Protocol for Energy-Efficient Wireless Sensor Network

In this COVID-19 pandemic situation, health care is on the priority of every human being. The recent development in the miniaturization of intelligent devices has opened many opportunities and played a crucial role in the healthcare industry. The amalgamation of wireless sensor network and Internet of Things is the best example of wireless body area network. These tiny sensor devices have two essential evaluation parameters named as energy efficiency and stability while performing in a group. This paper focuses on various issues of the healthcare system and their solutions. An energy-efficient routing protocol that can provide sensed data to the collection centre or data hub for further processing and treatment of the patients is proposed. Here, we fixed zones for sending data to zone head using distance aware routing, and then zone head send the aggregated data to the data hub. It is better than the low energy adaptive clustering hierarchy (LEACH) by 42% and distance-based residual energy-efficient protocol (DREEP) by 30% in energy efficiency and stability 58% more by LEACH and 39% by DREEP. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

WSNs and IoTs for the Identification of COVID-19 Related Healthcare Issues: A Survey on Contributions, Challenges and Evolution

In this era of technological revolution, we are familiar with many scientific terminologies and gadgets. Today, internet is the backbone of the whole world as internet connectivity plays a vital role in our routine life and makes our life much easier. Wireless sensors are implemented in many applications like agricultural sector, military, home automation and health care sector. These wireless sensors are easy to operate and handle. Their performance varies according to the application. By connecting internet with these smart wireless sensors they act like Internet of Things. In the present scenario, whole world is suffering from Covid-19 pandemic. This is very strenuous situation for mankind. It is enigmatic to recognize a person with Covid-19 symptoms. For the identification of affected patient, some models are coined with the aid of wireless sensors and internet of things. The principle goal of this survey is to demonstrate the critical role of wireless sensor networks with internet of things for Covid-19 health care purposes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Temperature Monitoring Mechanism for Covid-19 Using WSN

Internet of Things (IoT) and wireless sensor networks (WSN) are utilized in a variety of control systems, such as environmental monitoring, home automation, and chemical/biological assault detection. As 2019 finished, coronavirus sickness known as COVID-19 began multiplying all around the world has caused a disturbing circumstance worldwide. This infection causes pneumonia with different manifestations, for example fatigue, dry hack and fever. Early discovery of coronavirus might secure many contaminated people. This paper presents a plan of coronavirus observing component [COC] dependent on Wireless sensor organization and Internet of Things to screen individuals during their quarantine. Mechanism relies upon checking the patient wellbeing information when there is a high fever or troublesome in relaxing. © 2022 IEEE.

Improved Control System Based on PSO and ANN for Social Distancing for Patients With COVID-19

The World Health Organization has declared the COVID-19 pandemic, with most countries being affected by this virus both socially and economically. It thus became necessary to develop solutions to help monitor and control disease spread by controlling medical workers' movements and warning them against approaching infected individuals in isolation rooms. This paper introduces a control system that uses improved particle swarm optimization (PSO), and artificial neural network (ANN) approaches to achieve social distancing. The distance between medical workers carrying mobile nodes and the beacon node (isolation room) was determined using the ZigBee wireless protocol's received signal strength indicator (RSSI). Two path loss models were developed to determine the distance from patients with COVID-19: the first is a log-normal shading model (LNSM), and the second is a polynomial function (POL). The coefficient values of the POL model were controlled based on PSO to improve model performance. A random-nonlinear time variation controller-PSO (RNT-PSO) approach was developed to avoid the local minima of the conventional PSO. As a result, social distancing for COVID-19 can be accurately determined. The measured RSSI and the distance were used as ANN inputs, while three control signals (alarming, warning, and closing) were used as ANN outputs. The results revealed that the hybrid model between POL and RNT-PSO, called RNT-PSO-POL, improved the system's performance by reducing the mean absolute error of distance to 1.433 m, compared to 1.777 m for the LNSM. The results show that the ANN achieves robust performance in terms of mean squared error.

Route Planning using Wireless Sensor Network for Garbage Collection in COVID-19 Pandemic

Garbage collection is a responsibility faced by all cities and, if not properly carried out, can generate greater costs or sanitary problems. Considering the sanitary situation due to the COVID-19 pandemic, it is necessary to take sanitary safety measures to prevent its spread. The challenge of the present work is to provide an efficient and effective solution that guarantees a garbage collection that optimizes the use of resources and prioritizes the attention to garbage containers located in or near contagion risk zones. To this end, this research proposes the integration of a basic garbage monitoring system, consisting of a wireless sensor network, and a route planning system that implements the decomposition of the Vehicle Routing problem into the subproblems of clustering and sequencing of containers using the K-Means and Ant Colony algorithms. For the monitoring of garbage, a significant reduction in the measurement error of waste level in the containers was achieved compared to other authors. About route planning, adequate error ranges were obtained in the calculation of the optimal values of distance traveled and travel time indicators with respect to an exhaustive enumeration of routes. © 2022. All Rights Reserved.

Design of Wireless Sensor Network Programming Tutorial Module (WSNmesh32)

The use of Internet of Things technology may have a significant impact on changes in online learning systems in the situation of the COVID-19 pandemic. Learners need a laboratory to use and master the necessary tools or components. However, this is difficult to do in a pandemic situation. The use of remote laboratories can overcome the limitations of practicum media, which is an obstacle for students in learning. This research discusses the creation of WSNMesh32 programming tutorial modules as microcontrollers using Wi-Fi-mesh network topology that can be used in Sensor and Microcontroller Practicum courses. This research aims to find out the feasibility and perception of learners towards the WSNMesh32 Programming Tutorial Module as a learning medium in pandemic times. The method used is a quantitative method with the ADDIE model (Analyse, Design, Develop, Implement, Evaluation). Participants consisted of 31 students of the Electrical Engineering Education study program of the Industrial Electronics division 2018. The study was conducted online in light of the ongoing COVID-19 pandemic situation. The feasibility test of the module is carried out by leading discussions and reviews with experts on the material to be presented. Respondents gave a positive response to the module. The results provide the conclusion that the sensor programming tutorial module that has been produced is suitable for use as a tutorial module in sensor and microcontroller practicum courses and can be used in learning in a pandemic situation © 2021 IEEE.

Classification of COVID-19 Cases using Deep Neural Network based on Chest Image Data through WSN

Due to the rapid spread of corona virus disease (COVID-19), it has been considered as a pandemic throughout the world. The misclassification of COVID-19 cases may even lead the death of the patients, and hence the diagnosis at early stage is important to stop further spread of the infection and to safeguard the life of the patients. This paper proposes the Aquila tuned Deep neural network (Aquila-DNN) classifier for the classification of COVID-19 patients using the chest image data assessed through Wireless sensor Network (WSN). The extraction of important features from the chest image data is important in the diagnosis as it encloses the important data of the patients. The optimal tuning of the DNN parameters using the Aquila Optimizer (AO) assists in improving the classification accuracy of proposed model. In addition, the convergence is also boosted using the tuning process of the AO algorithm. The effectiveness of the proposed Aquila-DNN model is validated with the analysis of the model based on the performance indices, namely accuracy, ROC curve, and F1 measure. The testing accuracy and the training accuracy of Aquila-DNN model are attained to be 99.7%, and 95.4545%, respectively. © 2022, Ismail Saritas. All rights reserved.

Wireless Sensor Networks for Telerehabilitation of Parkinson’s Disease Using Rhythmic Auditory Stimulation

Parkinson’s Disease (PD) is a neurodegenerative disease affecting mainly the elderly. Patients affected by PD may experience slowness of movements, loss of automatic movements, and impaired posture and balance. Physical therapy is highly recommended to improve their walking where therapists instruct patients to perform big and loud exercises. Rhythmic Auditory Stimulation (RAS) is a method used in therapy where external stimuli are used to facilitate movement initiation and continuation. Aside from face-to-face therapy sessions, home rehabilitation programs are used by PD patients with mobility issues and who live in remote areas. Telerehabilitation is a growing practice amid the COVID-19 pandemic. This work describes the design and implementation of a wireless sensor network to remotely and objectively monitor the rehabilitation progress of patients at their own homes. The system, designed in consultation with a physical therapist, includes insole sensors which measure step parameters, a base station as a phone application which facilitates RAS training sessions and communication interface between the therapist and patients, and an online server storing all training results for viewing. Step data from the system’s real-time analysis were validated against post-processed and reconstructed signals from the raw sensor data gathered across different beats. The system has an accuracy of at least 80% and 72% for the total steps and correct steps respectively. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

WSN Based Health Monitoring System for COVID-19 Patients

The entire humankind is combating the COVID-19 pandemic since the onset of 2020. The increase in the cases has triggered an alarming situation all over the world. Doctors and hospital staff are working overtime in the COVID wards by putting their lives at risk. As the health line workers are in immediate contact with the patients, it causes a high risk of spreading the infection among them. To avoid immediate contact of the medical personnel with the patients, the work presented in this paper aims at devising wireless sensor network (WSN)-based COVID-19 patient health monitoring system, which is enabled with IoT and is useful in evaluating the patients, keeping a check on their vital/ health status without coming in contact with them, and eventually creating a feasible method of sending information related to the health of patients to the health staff. The doctors/nurses can keep an eye on the health stats and keep a check on the overall condition of the patients. In case hospital beds are not available, this system can also be used when the patient is home quarantined. The health personnel can take necessary action according to the parameters through mobile phone/desktop. The medical personnel may check these parameters through mobile phone or desktop and may take necessary action accordingly. Thus, the health monitoring system proposed in this paper helps monitor the data of patients remotely, and this decreases the need for contact between the health line workers and patients. This further lessens the possibility of infecting them. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Estimating Indoor Population Density from Non-contact Sensor Data

—With the spread of Covid-19, it is important to comprehend the indoor environment. It is also important to estimate indoor population density in order to avoid three Cs: Closed space, Crowded places, and Close-contact settings. This paper proposes a system for estimating indoor population density based on indoor environment data through Wireless Sensor Network (WSN). The proposed system collects indoor environment such as temperature, humidity, illuminace, CO2 concentration, and dust level. Then, the proposed system estimates indoor population density from collected environment data. The proposed system estimates the indoor population density from indoor environment data by machine learning. This study also investigates whether sensor data are adequate for estimating indoor population density. The experimental results show that the proposed system achieves about 80 % or more estimation accuracy by using multiple types of sensors, thereby demonstrating the effectiveness of the proposed system. The proposed system is expected to measure the closed space also estimate crowded rooms, which is a helpful finding for preventing the spread of COVID-19. © 2022 by the authors.

CheckING: An IoT Device for COVID-19 Monitoring and Prevention

The COVID-19 pandemic has spread rapidly around the world forcing people to isolate at home and collapsing hospitals causing millions of deaths. The continuous and efficient monitoring of those who showed symptoms jointly with the analysis of the environment conditions to avoid the spread of the virus gave rise to the development of different technological alternatives. In the present work, a comprehensive device with multi-parameter sensing has been designed, emphasizing the integration of physiological and environmental parameters with remote monitoring, of the interest in the current pandemic context. © 2021 IEEE.